AbstractNatural weather systems possess certain spatiotemporal variability and correlations. Preserving these spatiotemporal properties is a significant challenge in postprocessing ensemble weather forecasts. To address this challenge, several rank-based methods, the Schaake Shuffle and its variants, have been developed in recent years. This paper presents an extensive assessment of the Schaake Shuffle and its two variants. These schemes differ in how the reference multivariate rank structure is established. The first scheme (SS-CLM), an implementation of the original Schaake Shuffle method, relies on climatological observations to construct rank structures. The second scheme (SS-ANA) utilizes precipitation event analogs obtained from a historical archive of observations. The third scheme (SS-ENS) employs ensemble members from the Global Ensemble Forecast System (GEFS). Each of the three schemes is applied to postprocess precipitation ensemble forecasts from the GEFS for its first three forecast days over the mid-Atlantic region of the United States. In general, the effectiveness of these schemes depends on several factors, including the season (or precipitation pattern) and the level of gridcell aggregation. It is found that 1) the SS-CLM and SS-ANA behave similarly in spatial and temporal correlations; 2) by a measure for capturing spatial variability, the SS-ENS outperforms the SS-ANA, which in turn outperforms the SS-CLM; and 3), overall, the SS-ANA performs better than the SS-CLM. The study also reveals that it is important to choose a proper size for the postprocessed ensembles in order to capture extreme precipitation events.
Journal of Hydrometeorology – American Meteorological Society
Published: Mar 31, 2018
It’s your single place to instantly
discover and read the research
that matters to you.
Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.
All for just $49/month
Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly
Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.
Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.
Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.
All the latest content is available, no embargo periods.
“Hi guys, I cannot tell you how much I love this resource. Incredible. I really believe you've hit the nail on the head with this site in regards to solving the research-purchase issue.”Daniel C.
“Whoa! It’s like Spotify but for academic articles.”@Phil_Robichaud
“I must say, @deepdyve is a fabulous solution to the independent researcher's problem of #access to #information.”@deepthiw
“My last article couldn't be possible without the platform @deepdyve that makes journal papers cheaper.”@JoseServera